Sapphire

The Linking Open Data(LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 26 billion RDF triples. Consuming linked data is a challenging task because each data set in the LOD cloud has specific ontology schema, and familiarity with ontology schema is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experimental results show that our Mid-Ontology learning approach successfully integrates diverse ontology schema, and effectively retrieves related information.

There has recently been an increase in the number of RDF knowledge bases published on the Internet. These rich RDF data sets can be useful in answering many queries, but much more interesting queries can be answered by integrating data from different data sets. This has given rise to research on automatically linking different RDF data sets representing different knowledge bases. This is challenging due to the scale and semantic heterogeneity of these data sets. Various approaches have been proposed, but there is room for improving the quality of the generated links. In this demonstration, we showcase ALEX, a system that aims at improving the quality of links between RDF data sets by using feedback provided by users on the answers to linked data queries. ALEX starts with multiple RDF data sets that are linked using any automatic linking algorithm. ALEX enables the user to issue queries that integrate data from different data sets, and to provide feedback on the answers to these queries. ALEX uses this feedback to eliminate incorrect links between the data sets and discover new links. In this demonstration, we show ALEX in action on multiple data sets from the Linked Open Data cloud.

Geospatial datasets in the Linking Open Data (LOD) Cloud are rather of static nature and mainly consist of information such as a name, geo coordinates, an address, or opening hours. There is no linked dataset providing dynamic information about the "popularity" of certain places or the "Visiting frequency" of users in specific contextual situations. This type of information within the LOD Cloud, however, would enable a variety of new applications based on semantically enriched location analytics. In this paper, we present Linked Crowdsourced Data as a dataset, which links real user location preferences (e.g., check-ins, ratings, or comments) as well as specific context situations (e.g., weather conditions, holiday information, or measured networks) collected via crowdsourcing to static location data. We showcase the applicability of this dataset for location analytics use cases through a map visualization and highlight its added value with exemplary SPARQL queries that allow for location requests depending on historic context information.

This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. The second part of the paper shows, through a comprehensive set of the spatial extension to SPARQL (stSPARQL) queries, how the endpoint can be exploited.

Linked Open Data (LOD) is a huge effort in the direction of making the Web of Data a reality. The LOD cloud consists of about 200 datasets contributed by several independent data providers from various domains such as Publications, Geography, Government, Media, Biology and Drugs etc. The data is represented in RDF and SPARQL is the query language. Due to the open nature of the data and its heterogeneous origin, it is often the case that a real-world entity appears in different datasets and with different names. When such entities are to be reported in the query results, a mechanism of rank ordering them becomes essential. In this paper, we propose a new framework for calculating the importance scores of datasets and also resources inside the datasets. As consensus is a key element that adds value to the data in the Web of Data, we base our ranking framework on constructs that are used to express sameness or equivalence among the entities in RDF data. We also make use of the underlying graph structure of LOD in the framework. The framework is experimentally verified on the Billion Triple Challenge (BTC-2010) dataset. The results indicate that the framework is successful in giving entities from the most relevant dataset a higher score compared to other datasets.

In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. According to our knowledge this is the first work that combines social analytics with the Linked Open Data (LOD) cloud.

Establishing correct links among the coreference ontology instances is critical to the success of Linked Open Data (LOD) cloud. However, because of the high level heterogeneity and large scale instance set, matching the coreference instances in LOD cloud is an error prone and time consuming task. To this end, in this work, we present an asymmetrical profile-based similarity measure for instance matching task, construct new optimal models for schema-level and instance-level matching problems, and propose a compact hybrid evolutionary algorithm based ontology matching approach to solve the large scale instance matching problem in LOD cloud. Finally, the experimental results of comprising our approach with the states of the art systems on the instance matching track of OAEI 2015 and real-world datasets show the effectiveness of our approach.

Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summaries from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF data with tens of millions of triples. Furthermore, the existing summarization techniques may lead to incorrect/incomplete results. To address these issues, we propose an effective summarization algorithm to summarize the RDF data. Given a keyword query, the summaries lend significant pruning powers to exploratory keyword search and result in much better efficiency compared to previous works. Unlike existing techniques, our search algorithms always return correct results. Besides, the summaries we built can be updated incrementally and efficiently. Experiments on both benchmark and large real RDF data sets show that our techniques are scalable and efficient.

Existing content management systems (CMSes) usually do not offer flexible, customizable means to create semantic, domain specific indexing and search mechanisms. Therefore, they either do not provide any semantic retrieval, search, browsing functionalities at all on the managed content or the semantic search functionality provided is limited as it depends on the manual annotation of content by users. So, in this study we describe a semantic content management flow by extracting implicit knowledge from both the structure of the CMSes and actual content within them. The task of additional semantic knowledge gathering and providing semantic operations on the content is a challenging task which includes adoption of several latest advancements in information extraction (IE), information retrieval (IR) and Semantic Web areas. In this study, we propose a new approach which provides automatic annotation of content managed in CMSes with the information retrieved from the Linked Open Data (LOD) cloud and several semantic operations on the content in terms of storage and search. We use a simple RDF path language to create custom indexes and retrive semantic knowledge from the LOD cloud suitable for specific use cases. All additional knowledge is materialized along with the actual content of document in dedicated indexes. This semantix indexing infrastructure allows semantically meaningful search facilities on top of it. We realize our approach in the scope of Apache Stanbol project, which is a subproject developed in the scope of IKS project, by focusing on document storage and retrieval. We evaluate our approach in healthcare domain with different domain ontologies (SNOMED/CT, ART, RXNORM) in addition to DBpedia as parts of LOD cloud which are used to annotate documents and content obtained from different health portals.

With the popularity of Linked Open Data (LOD) and the associated rise in freely accessible knowledge that can be accessed via LOD, exploiting LOD for recommender systems has been widely studied based on various approaches such as graph-based or using different machine learning models with LOD-enabled features. Many of the previous approaches require construction of an additional graph to run graph-based algorithms or to extract path-based features by combining user-item interactions (e.g., likes, dislikes) and background knowledge from LOD. In this paper, we investigate Factorization Machines (FMs) based on particularly lightweight LOD-enabled features which can be directly obtained via a public SPARQL Endpoint without any additional effort to construct a graph. Firstly, we aim to study whether using FM with these lightweight LOD-enabled features can provide competitive performance compared to a learning-to-rank approach leveraging LOD as well as other well-established approaches such as kNN-item and BPRMF. Secondly, we are interested in finding out to what extent each set of LOD-enabled features contributes to the recommendation performance. Experimental evaluation on a standard dataset shows that our proposed approach using FM with lightweight LOD-enabled features provides the best performance compared to other approaches in terms of five evaluation metrics. In addition, the study of the recommendation performance based on different sets of LOD-enabled features indicate that property-object lists and PageRank scores of items are useful for improving the performance, and can provide the best performance through using them together for FM. We observe that subject-property lists of items does not contribute to the recommendation performance but rather decreases the performance.

Linked Open Data (LOD) Cloud is a mesh of open datasets coming from different domains. Among these datasets, a notable amount of datasets belong to the life sciences domain linked together forming an interlinked “Life Sciences Linked Open Data (LSLOD) Cloud”. One of the key challenges for data publishers is to identify and establish links between newly generated domain specific datasets and LSLOD Cloud. While a number of publishing tools exist for creating links from new to existing datasets, tools to detect domain-specific relevant datasets for linking purposes are missing. In this paper, we propose an extended technique for automatically identifying relevant datasets in LSLOD Cloud for inner-ear anatomical and clinical terminologies. We validate the proposed technique with experiments over the publicly accessible LSLOD Cloud using realworld terminologies and datasets provided by clinical organizations.

Publication and Usage of Official Czech Pension Statistics Linked Open Data

Usually the content of the dataset published as LOD is rather unknown and data publishers have to deal with the challenge of interlinking new knowledge with existing datasets. Although there exist tools to facilitate data interlinking, they use prior knowledge about the datasets to be interlinked. In this paper we present a framework to profile the quality of owl:sameAs property in the Linked Open Data cloud and automatically discover new similarity links giving a similarity score for all the instances without prior knowledge about the properties used. Experimental results demonstrate the usefulness and effectiveness of the framework to automatically generate new links between two or more similar instances.

We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents.

Binary RDF Representation for Publication and Exchange (HDT)